Hu Sheng second-place award in the 2022 CPGIS Best Student Paper competition at the 29th International Conference on Geoinformatics, Beijing, China. The title of his presentation is Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data. In this study, we employed the Word2Vec model to quantify fine-scale interaction relations of road segments on a road network using taxi trajectory data. We then utilized the Infomap community detection method to reveal significant regional patterns in the Wuhan metropolitan area. We found the three-level hierarchical structures that indicate the division of urban space with the integration of a large volume of real taxi trajectories into the urban road network. Both our method and results can provide traffic managers and urban planners with a better understanding of traffic patterns on the road network and the regional structure of a city. This work is supervised by Prof. Luo Wei, Prof. Gao Song at University of Wisconsin-Madison, and Prof. Wu Liang at China University of Geosciences.
